The Studyand Implementation Of Semantic Expanded Search Approach Based On Ontology For Knowledge Base

The knowledge base is the foundation of knowledge management.At present, the knowledge base search is mainly based on thecombination of reasoning and keywords matching search, but the retrievalefficiency is not high enough because of the vague expressions anddeficient search words of users, which does not fully satisfy the peopleneeds of information retrieval. Semantic web ontology technology andquery expansion techniques can effectively improve the problem.The semantic expanded search approach based on ontology forknowledge base is proposed in this paper, combining ontologytechnology and query expansion techniques and then adding bothsemantic and statistical expanded search terms into the user query. Theuser query words are divided into accurate sets and fuzzy sets. Expandthe accurate set of query words with ontology logical reasoning, add thestandardized associated information for the query to receive the ontologyexpanded sets; Process fuzzy sets with local context analysis, take themost relevant top m words from the most relevant top n articles, fuzzy sets are turned into accurate sets, and get statistical expanded word setsinto reasoning expansion recursively; Get the search results according toexpanded sets, and take advantage of the correlation analysis presented inthis paper, sort the search results; At last, build the museum knowledgebase system based on the semantic expanded search approach.Experiments results show that compared with other search approaches inthe expriments, the proposed approach can effectively increase the recallrate in the case of similar precision with other approaches.The semantic expanded search approach based on ontology forknowledge base is proposed by this paper, which is based on ontologyand query expansion algorithm research. At the same time, experimentsprove the feasibility of this approach, which will play a promoting role inthe field of semantic expansion.